Table of Contents
ISRN Biomedical Imaging
Volume 2013 (2013), Article ID 504594, 12 pages
http://dx.doi.org/10.1155/2013/504594
Research Article

Segmentation of Scarred Myocardium in Cardiac Magnetic Resonance Images

1Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway
2Cardiology Department, Stavanger University Hospital, 4011 Stavanger, Norway

Received 10 October 2013; Accepted 12 November 2013

Academic Editors: B. Tomanek and G. Waiter

Copyright © 2013 Lasya Priya Kotu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The segmentation of scarred and nonscarred myocardium in Cardiac Magnetic Resonance (CMR) is obtained using different features and feature combinations in a Bayes classifier. The used features are found as a local average of intensity values and the underlying texture information in scarred and nonscarred myocardium. The segmentation classifier was trained and tested with different experimental setups and parameter combinations and was cross validated due to limited data. The experimental results show that the intensity variations are indeed an important feature for good segmentation, and the average area under the Receiver Operating Characteristic (ROC) curve, that is, the AUC, is 91.58 ± 3.2%. The segmentation using texture features also gives good segmentation with average AUC values at 85.89 ± 5.8%, that is, lower than the direct current (DC) feature. However, the texture feature gives robust performance compared to a local mean (DC) feature in a test set simulated from the original CMR data. The segmentation of scarred myocardium is comparable to manual segmentation in all the cross validation cases.